The State of Algorithmic Fairness in Mobile Human-Computer Interaction
Not on the map. Why?
Abstract (arXiv)
This paper explores the intersection of Artificial Intelligence and Machine Learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive analysis of MobileHCI proceedings published between 2017 and 2022, we first aim to understand the current state of algorithmic fairness in the community. By manually analyzing 90 papers, we found that only a small portion (5%) thereof adheres to modern fairness reporting, such as analyses conditioned on demographic breakdowns. At the same time, the overwhelming majority draws its findings from highly-educated, employed, and Western populations. We situate these findings within recent efforts to capture the current state of algorithmic fairness in mobile and wearable computing, and envision that our results will serve as an open invitation to the design and development of fairer ubiquitous technologies.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 22 Jul 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2024CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographicscited by 1
- 2024I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Datacited by 0
- 2025FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelinescited by 0
- 2026Ethical Fairness in Ubiquitous Health Sensing without Known Attributescited by 0
- 2026Ethical Fairness in Ubiquitous Health Sensing without Known Attributes10 Mar 2026
- 2025FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines10 Jun 2025
- 2024I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data10 Jun 2024
- 2024CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics30 Apr 2024